In the semiconductor manufacturing process environment, there are several different distributed data monitors used to gather information (typically in the form of data streams) about different steps in the manufacturing process, tool operation, wafer defects, test results, etc. Significant improvements in the performance of this manufacturing process are achieved by appropriately analyzing the available streams using statistical techniques, and using this to drive process control.
There has been a large amount of work on developing the appropriate statistical analytic solutions for different kinds of gathered data. An important statistical process control (SPC) method uses multivariate analysis on the time series and thresholds the resulting summary statistics to detect out-of-specification tool parameters, and uses this to control the tool operation. However, most of these schemes perform analysis on data gathered from one tool, i.e., analysis is performed on a data stream from one tool at a time. This leads to limitations in the SPC performance, as it deters cross-tool, cross-step, and cross-data-source analysis.